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Barley (Hordeum dischon L. and Hordeum vulgare L.) is a multipurpose plant cultivated since ancient time for food, feed, medicinal purposes and malt of alcoholic beverages. Stability parameters are useful tools for identification of genotypes with specific and wide adaptations, and contrasting the role played by genotype, environment and G x E interaction in multilocational variety trials. Interaction principal component axis (IPCA) scores, Additive main effect and multiplicative interaction stability value (ASV i ), Wricke's ecovalence (W i ), regression coefficient, coefficient of variation (CV i ), genotypic/environmental variance (S i 2 ), stability variance (s i 2 ) and cultivar/environment superiority measure(P i ) were used to evaluate the yield performance and stability of twenty malting barley genotypes in twelve rain-fed environments during [2005][2006][2007]. Spearman rank correlation showed that b j , R i 2 , S j 2 , CV j , and IPCA 1 of environments were positively correlated, indicating that any of these five parameters can be used as a good alternative for stability evaluation. These stability parameters were positively correlated with mean yield of environments. The mean of genotype yields were positively correlated with stability parameters of b i and R i 2 (P<0.01), but were negatively correlated with IPCA 1 , W i 2 , P i (P<0.01) and ASV i . Based on these parameters, genotypes G1 and G13 combined high and stable grain yield, whereas the highest yielding genotype G12 was not stable.

The study was conducted with the objectives to determine the magnitude effect of genotype, environment, and their interactions on economically important traits and identify stable malt barley (Hordeum distichon L.) genotypes. Combined analysis of variance indicated that the main effects due to environment, genotype and GxE interaction were highly significant for grain yield and economically important malting quality triats indicated that development of both specific and wide adaptable varieties are essential. The GxE interaction of grain yield was further partitioned using AMMI and it showed the first two IPCA axes explained most of the sum of squares. According to stability analysis measures genotype G1 was the most stable whereas G13 showed specific adaptation in low potential environments. Protein content and seed size variability measures revealed G9 and G11 in protein content and G1 and G11 in seed size, respectively as the least varying genotypes across environments.

The study was conducted to identify determinants of losses during pre-harvest and postharvest activities of fruits and their extent at producer`s level in Northwestern Ethiopia where tomato, papaya, avocado, banana and mango were used as fruit samples. Questionnaires were used to collect data from 180 randomly selected respondents of six districts (FinoteSelam, BurieZuria, Bahir Dar, Bahir Dar Zuria, Dangla and Farta). Descriptive statistics and multiple regressions analysis were used to identify determinant factors. The results of the findings revealed that the total fruit loss was estimated to be 44.8% where about 20.7% of the fruits were lost due to improper activities in the pre-harvest stages while about 24.1% loss was due to improper activities during post-harvest stages. The shares of pre-harvest and postharvest losses to the total fruit loss were about 46.2% and 53.8%, respectively. Income sources, use of pesticide, and use of compost or manure during production were the determinant factors that influenced fruit losses during pre-harvest while experience and educational levels of producers in fruit production and shortage of labor were the determinant factors of fruit losses during harvesting. Moreover, chemical treatments of fruits before storage and educational levels of the producers were the determinants that influenced fruit losses in producer`s storage while experiences of the fruit producers, distance to market and educational levels were the significant factors of fruit losses during marketing. Further researches and trainings of producers about use of pre-and postharvest technologies that minimize losses at the value chain of fruits are vital. Contribution/Originality:This survey is one the few researches that has estimated fruit loss and identified the major factors responsible for loss. The research paper contribution is finding that about 44.8 percent fruit is lost and use of pre-and post-harvest technologies as well as educating producers about fruit loss is vital. INTRODUCTIONAgriculture is the mainstay of Ethiopian economy. The sector provides raw materials for industries and the main source of products for export market. The country's agricultural potential is known to be immense and over 90% of its export earnings come from this sector. Coffee, oil seeds, spices, fresh fruit and vegetables contribute the largest portion of the export earnings. From a total of 39.7 million tons of total crops produced in Ethiopia, about 23.1 million tons are durable crops while about 6.6 million tons are highly perishable. Of which about 0.5 million tons are tropical fruits including Tomato, Banana, Mango, Papaya, Avocado, Guava and Pineapple which are highly perishable (CSA (Central Statistical Authority), 2013).

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